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Is there a genetic relationship between blood glucose and osteoarthritis? A mendelian randomization study

Diabetology & Metabolic Syndrome volume 16, Article number: 274 (2024) Cite this article

Abstract

Objective

The relationship between blood glucose levels and osteoarthritis (OA) is unclear. This study aimed to investigate the genetic causal relationship between blood glucose-related traits and OA.

Methods

We first performed univariate Mendelian randomization (UVMR) analyses using published genome-wide association study (GWAS) datasets with fasting glucose (FG), 2 h-glucose post-challenge glucose (2hGlu), and glycosylated hemoglobin (HbA1c) as exposures, and hip osteoarthritis (HOA) and knee osteoarthritis (KOA) as outcomes; then, we performed inverse analyses of them. We used Inverse-variance weighted (IVW) analysis as the primary analysis, and sensitivity analyses were performed. Moreover, we performed multivariate Mendelian randomization (MVMR) to estimate the independent effect of exposure on outcome after adjusting for body mass index (BMI). Summarized data for blood glucose-related traits were obtained from the MAGIC Consortium study of the glucose trait genome and for OA from the UK Biobank and arcOGEN. Summarized data for BMI were obtained from the GIANT Consortium meta-analysis of individuals of European ancestry. A two-sided p value < 0.05 in UVMR was considered suggestive of significance when p < 0.0167 (Bonferroni correction p = 0.05/3 exposures) was considered statistically significant.

Results

We found significant negative genetic causality of FG for HOA and KOA, and these associations remained significant after we adjusted for the effect of BMI [odds ratios (ORs) of 0.829 (0.687–0.999, p = 0.049) and 0.741 (0.570–0.964, p = 0.025)]. HbA1c also had an independent negative genetic causal effect on HOA after adjustment for BMI [0.665 (0.463–0.954, p = 0.027)]. At the same time, there was no evidence of reverse genetic causality of OA on blood glucose-related traits.

Conclusion

We further elucidated the relationship between blood glucose-related traits and OA by adjusting for the effect of BMI from a genetic causal perspective. This study provides new insights to further clarify the relationship between blood glucose levels and OA, as well as the pathogenesis, etiology and genetics of OA.

Introduction

Osteoarthritis (OA) affects > 500 million people, which is the most common degenerative joint disease. It is a major source of pain and joint dysfunction worldwide and contributes to higher socioeconomic costs [1, 2]. OA occurs mostly in the knee and hip joints and is accompanied by features such as cartilage degeneration, subchondral bone changes, and synovitis. The prevalence of OA increases with the aging of the population and increasing rates of obesity, further increasing the disease burden. However, for patients with advanced OA, there is no effective treatment other than total joint replacement [3]. OA results from the interaction of multiple factors, such as age, trauma, and genetic susceptibility, and its pathogenesis has not yet been fully elucidated. Therefore, it is necessary to explore the potential factors that affect the development of OA and prevent OA by regulating these factors. Previous studies have shown that metabolic syndrome is closely related to OA and is a risk factor for OA [4, 5].

Glucose is an important source of energy for the body and is tightly regulated to meet body needs and maintain health [6]. As part of metabolic syndrome, the relationship between dysglycemia and OA has also received attention, but the findings have been inconsistent. Some clinical studies have reported that impaired glucose homeostasis is associated with typical knee osteoarthritis (KOA) and predicts an individual risk of occurrence [7]. In addition, large meta-analyses have shown that diabetes mellitus (DM) is significantly associated with OA, with DM population having a higher risk of OA than non-DM population [8]. However, some researchers have also found little evidence that impaired glucose metabolism is an independent risk factor for knee OA compared with obesity or that it is an independent risk factor for hip or hand OA [9]. Another meta-analysis of antecedent diabetes and OA concluded that antecedent diabetes was not associated with overall osteoarthritis, knee or hip osteoarthritis [10]. The results of these studies are inconsistent, and it is unclear whether there is a genetic causal relationship between fasting glucose levels and OA.

Mendelian randomization (MR) using genetic variants (single nucleotide polymorphisms, SNPs) that are closely associated with potential risk factors as instrumental variables (IVs), is an epidemiological research method used to study the causal relationship between exposures and outcomes [11]. Alleles are randomly assigned at meiosis following Mendelian laws, forming natural exposure and control groups, which are highly similar to randomized controlled trials (RCTs) [12]. In contrast to traditional observational studies, MR studies are less susceptible to bias owing to confounding factors because genetic variants are randomly assigned. Genotypic decisions occur independently before the environment influences an individual, which prevents reverse causation [13].

Most of the past studies used observational approaches, and these studies varied in quality, design, population, and outcome estimates, which could lead to confusion. Our study was based on available genetic databases, and MR analyses of blood glucose-related traits and osteoarthritis [hip osteoarthritis (HOA) and KOA] were performed using genome-wide association study (GWAS) dataset to explore the possible genetic causality between them.

Methods and materials

Study design

This study aimed to investigate the genetic causality between blood glucose-related traits [fasting glucose (FG), 2 h-glucose post-challenge (2hGlu), and glycated hemoglobin (HbA1c)] and OA. Mendelian randomization using SNPs as IVs, and the IV was selected to satisfy the following three assumptions: (1) it is significantly associated with the exposure, (2) it is not associated with any confounders influencing the exposure–outcome relationship, and (3) it is associated with the outcome only through the exposure [11]. In our study, we first applied two-sample univariate Mendelian randomization (UVMR) to bidirectional analyses of blood glucose-related traits and OA (HOA, KOA) to explore the genetic causality between them. Obesity, as an important risk factor for OA, may also have some associations with blood glucose, so we also performed multivariate Mendelian randomization (MVMR) to estimate the independent effects on the relationship between blood glucose-related traits and OA after adjusting for body mass index (BMI). The basic design framework is illustrated in Fig. 1.

Fig. 1
figure 1

Design framework of the Mendelian randomization study. FG: fasting glucose; 2hGlu: 2 h-glucose post-challenge; HbA1c: glycated hemoglobin; HOA: hip osteoarthritis; KOA: knee osteoarthritis; BMI: body mass index; UVMR: univariate Mendelian randomization; MVMR: multivariate Mendelian randomization

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Data sources

Genome-wide association studies (GWAS) of FG, 2hGlu, and HbA1c summary data were obtained from the Chen et al. 2021 study of glycemic trait genomes [14]. They pooled genome-wide association studies of up to 281,416 individuals without diabetes (30% non-European ancestry). Summary results were obtained from the meta-analysis of 200,622 individuals of European ancestry by visiting the MAGIC website (https://www.magincvestigators.org/).

We obtained summary data for HOA and KOA from a genome-wide analysis study of the UK Biobank [15]. The study utilized the UK Biobank and arcOGEN resources to conduct a genome-wide meta-analysis of approximately 17.5 million single-nucleotide variants in up to 455,221 individuals. For HOA included 15,704 patients and 378,169 controls, and For KOA included 24,955 patients and 378,169 controls.

GWAS summary data for BMI from a meta-analysis of 322,154 individuals of European ancestry by Locke, A. E. et al. [16]. This study identified 97 nearly independent SNPs (p < 5 × 10− 8) associated with BMI. We obtained specific outcome data by visiting the GIANT Consortium website. The study did not involve the UK Biobank cohort, avoiding sample overlap with outcomes.

This study was based on public databases and published publications, and all studies were approved by the relevant ethical review boards. All participants provided informed consent, so no additional ethical approval or consent to participate was needed.

Instrumental variables selection

In order to find IVs with sufficient validity and satisfying the three basic assumptions of MR, a rigorous process of screening was employed: (1) screening for SNPs significantly associated with exposure at the genome-wide level (p < 5 × 10 − 8); (2) the effect of linkage disequilibrium was removed (r2 < 0.001, window size = 10,000 kb): (3) SNPs associated with confounders (body mass index) were screened and excluded using the PhenoScanner database (http://www.phenoscaner.medschl.cam.ac.uk/) (p < 5 × 10 − 8)(4) SNPs significantly associated with the outcome (p < 5 × 10 − 8) were excluded to avoid pleiotropy of IVs(5) palindromic variants that could not be oriented (i.e., C/G or A/T) were also removed. (6) we calculated the F-values of the IVs, and SNPs with F < 10 were removed to avoid weak instrumental variable bias [17].

Statistical analysis

In this study, we applied multiple methods to estimate the causal effects of exposure on outcomes. For UVMR, the primary analysis used inverse-variance weighted (IVW) method, and the secondary analysis used MR‒Egger method, weighted median method, and weighted model method. The IVW method is based on the assumption that all IVs are valid, and provides the most accurate estimates [18]. The weighted median method provides consistent estimates when no more than half of the invalid instrumental variables [19]. The MR‒Egger method allows horizontal pleiotropy and correction for this in MR analysis but with lower precision [20]. Although the weighted model is not as strong an effect estimate as IVW, it can be used as part of the sensitivity analysis [21]. A two-sided p value < 0.05 for UVMR was considered suggestive of significance, and a p value < 0.0167 (Bonferroni correction p = 0.05/3 exposures) was considered statistically significant.

We used a variety of sensitivity analyses to assess estimates of genetic causality effects. We calculated the I [2] index and Cochran’s Q statistic to assess the heterogeneity of the IVW method. The intercept of the MR-Egger method demonstrates the potential horizontal pleiotropy between IVs [22]. In addition, MR-PRESSO was applied, which detects and corrects for horizontal pleiotropy and returning corrected causal effects by removing outliers at p < 0.05 [23]. Potential pleiotropy was also considered if the funnel plot did not show symmetry [24]. Leave-one-out analysis was also used to assess the reliability of the results by detecting the effect of individual SNP to the overall estimates [25]. To avoid reverse causality between exposure and outcome, the Steiger test was also used [26]. MVMR was performed to determine the effect of exposure on outcomes after adjusting for BMI.

All statistical analyses were performed in R software (version 4.3.1) using the TwoSampleMR package and the MendelR package.

Results

Through our strict screening process described above and the removal of confounders, a sufficient number of SNPs were identified for MR analysis for different exposures and outcomes, and the characterization and detailed information of the SNPs that were used as IVs are presented in Table 1 and the Supplementary Material.

Table 1 Overview of single-nucleotide polymorphisms used in the UVMR analysis
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Univariate mendelian randomization

Blood glucose-related traits for OA

We first performed UVMR with FG, 2hGlu, and HbA1c as exposures and HOA as the outcome. The effect estimates obtained using the different methods are displayed in Fig. 2. When FG was used as the exposure, the four MR methods all returned negatively correlated estimates of HOA, and the results were statistically significant [OR (IVW): 0.63 (0.46–0.86], p = 0.003]. For 2hGlu, the analysis of UVMR also showed a suggestive significant negative correlation with HOA [OR (IVW):0.76 (0.60–0.96), p = 0.020]. However, there was no significant genetic causality between HbA1c and HOA [OR (IVW): 0.79 (0.56–1.10], p = 0.156]. Scatter plots and leave-one-out validation plots of the UVMR analysis of blood glucose-related traits with HOA are shown in Fig. 3 and the Supplementary Material.

Fig. 2
figure 2

Results of UVMR analysis of blood glucose-related traits and OA. FG: fasting glucose; 2hGlu: 2 h-glucose post-challenge; HbA1c: glycated hemoglobin; HOA: hip osteoarthritis; KOA: knee osteoarthritis; SNP: single nucleotide polymorphism; CI: confidence interval; OR: odds ratio

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Among the sensitivity analyses, the Cochran’s Q test for FG (2hGlu) was p < 0.05, I2 = 0.41 (0.57), and the Cochran’s Q test for HbA1c was p = 0.06, I2 = 0.40, suggesting heterogeneity in the analyses (p = 0.06, I2 = 0.40); however, MR‒Egger regression revealed that there was no horizontal pleiotropy in the study (Egger Intercept, p > 0.05), which fulfilled the InSIDE hypothesis, and a certain level of heterogeneity was acceptable in this scenario [27]. The MR-PRESSO test did not reveal any outliers. p < 0.001 in the Steiger test indicated no reverse causality. The results of the sensitivity analyses are presented in Table 2.

We then performed UVMR with FG, 2hGlu, and HbA1c as exposures and KOA as the outcome. The effect estimates obtained using the different methods are displayed in Fig. 2. With FG as an exposure, all MR methods consistently resulted in estimates of negative associations, but the results were not statistically significant [OR (IVW):0.95 (0.72–1.25), p = 0.693]. When using 2hGlu and HbA1c as exposures, the different MR methods returned directionally inconsistent estimates, which were similarly not statistically significant [2hGlu: OR(IVW):1.07 (0.93–1.24), p = 0.340; HbA1c: OR(IVW):1.02 (0.76–1.36), p = 0.913]. Images of the scatterplots and leave-one-out validation are shown in the Supplementary Material. Among the sensitivity analyses, Cochran’s Q test and I [2] suggested some heterogeneity. However, the MR‒Egger regression showed no horizontal pleiotropy in the study (Egger Intercept, p > 0.05). The MR-PRESSO assay also revealed no outliers, and no reverse causality in Steiger test (p < 0.001). The results of the sensitivity analyses are presented in Table 2.

Fig. 3
figure 3

Scatterplot of UVMR analysis of blood glucose-related traits with OA. FG: fasting glucose; 2hGlu: 2 h-glucose post-challenge; HbA1c: glycated hemoglobin; SNP: single nucleotide polymorphism

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OA for blood glucose-related traits

We performed UVMR with HOA, KOA as exposures, and blood glucose-related traits as outcomes, respectively, and the effect estimates obtained using the different methods are shown in Fig. 4. No results suggestive of significance for blood glucose-related traits were found when HOA was used as an exposure. No results suggestive of significance were found for FG and 2hGlu when KOA was the exposure. However, a significant positive genetic causal relationship was found between KOA and HbA1c [OR (IVW): 1.029 (1.011–1.047], p = 0.001]. Scatter plots and leave-one-out validation plots of UVMR analyses of OA and blood glucose-related traits are displayed in Fig. 5 and Supplementary Material. In sensitivity analysis, Cochran’s Q test with KOA as exposure and HbA1c as outcome p = 0.64, I2 = 0, indicating no heterogeneity in the analysis. MR‒Egger regression showed no horizontal pleiotropy in the study (Egger Intercept, p > 0.05). MR-PRESSO test also did not reveal any outliers. In Steiger test, p < 0.001 showed no reverse causality. The results of the specific sensitivity analyses are displayed in Table 2.

Fig. 4
figure 4

Results of UVMR analysis of OA and blood glucose-related traits. FG: fasting glucose; 2hGlu: 2 h-glucose post-challenge; HbA1c: glycated hemoglobin; HOA: hip osteoarthritis; KOA: knee osteoarthritis; SNP: single nucleotide polymorphism; CI: confidence interval; OR: odds ratio

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Fig. 5
figure 5

Scatterplot of UVMR analysis of blood OA with glucose-related traits

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Table 2 Sensitivity analysis of the UVMR results
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Multivariate mendelian randomization

As obesity may have an effect on both OA and glucose levels, we applied the MVMR to determine the independent genetic effects on outcomes after adjusting for BMI, and the results are displayed in Fig. 6. Negative genetic causal effects of FG on KOA, and HbA1c on HOA, changed from non-significant to significant after adjusting for the effect of BMI. ORs were 0.829 (0.687–0.999, p = 0.049) and 0.665 (0.463–0.954, p = 0.027), respectively. The significant negative genetic causal effect of FG on HOA remained significant after adjusting the effect of BMI [OR: 0.741 (0.570–0.964), p = 0.025]. When OA was the exposure, along with BMI, for MVMR of blood glucose-related traits, none of the analyses returned significant results. KOA, which was significant in the previous UVMR, no longer had a significant effect on HbA1c after adjusting for the effect of BMI [OR: 1.014 (0.985–1.044), p = 0.349].

Fig. 6
figure 6

MVMR analysis of blood glucose-related traits with OA. FG: fasting glucose; 2hGlu: 2 h-glucose post-challenge; HbA1c: glycated hemoglobin; HOA: hip osteoarthritis; KOA: knee osteoarthritis; SNP: single nucleotide polymorphism; CI: confidence interval; OR: odds ratio

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Discussion

This study applied MR methods to investigate the genetic causal relationship between blood glucose-related traits and OA. Analyses showed significant negative genetic causality for FG on HOA and KOA, and this association remained significant after adjusting for the effect of BMI. HbA1c also had an independent negative genetic causal effect on HOA after adjusting for BMI. There was insufficient evidence for an association between 2hGlu and OA. Analyses of OA on blood glucose-related traits indicated no reverse genetic causality between them. Our study explored the genetic causality between blood glucose-related traits and OA, helping to further elucidate the association between blood glucose and OA, as well as contributing to the pathogenesis, etiology, and genetics of OA.

The pathogenesis of OA is complex and metabolic factors play an important role. The complex clinical challenges arising from the co-morbidity of osteoarthritis and type 2 diabetes mellitus have raised concerns [28]. However, the role of abnormal blood glucose and diabetes in the development of osteoarthritis have not yet been clearly established. The latest research suggests that the TyG index, which represents insulin resistance, is associated with an elevated risk of OA in individuals with sarcopenic obesity [29]. A case-control study by Driban et al. found that impaired glucose homeostasis was associated with the occurrence of typical KOA events in individuals but not accelerated KOA [7]. Another prospective analysis with a median follow-up of 5 years also found that DM and poor glycemic management were associated with a higher risk of developing knee osteoarthritis independent of age and body mass index [30]. However, more studies have concluded that the association between glycemic abnormalities and OA is not as strong as expected. A meta-analysis that consistently included more than 40,000 individuals showed that antecedent diabetes mellitus (defined as impaired fasting glucose, impaired glucose tolerance, or an HbA1c of 39–46 mmol/mol) is not associated with overall OA, HOA, or KOA [10]. An analysis of participants from the multicenter osteoarthritis study by Rogers-Soeder et al. found that DM and higher levels of abnormal biomarkers of glucose metabolism were not associated with increased odds of radiographic KOA after adjustment for BMI [31]. The results of another prospective cohort study with 32 years of follow-up did not support the hypothesis that the metabolic syndrome component increases the risk of KOA, and also found that higher fasting glucose was associated with a reduced risk, similar to our results [32]. A previous MR analysis using earlier published GWAS data also found a non-significant negative genetic causality between FG and OA [33].

Chondrocytes in joints are non-vascularized tissues with nutrients supplied by joint fluid. Their environment is relatively hypoxic, and their main source of energy is glycolysis; glucose metabolism homeostasis is critical for chondrocyte energy homeostasis [34]. Animal experiments have revealed a significant reduction in the expression of Glut1, the major glucose transporter protein in articular cartilage, in a surgically induced mouse model of KOA, which accelerates cartilage loss in OA [35]. A low degree of inflammation is also a feature of OA, and the infiltration of inflammatory cells as well as increased secretion of proinflammatory cytokines further contributes to the production of protein hydrolases and cartilage degeneration [36]. Cellular experiments by Sopasakis et al. found that higher glucose concentrations favor glucose uptake by chondrocytes and the maintenance of matrix integrity after stimulation with the inflammatory factor IL-1β, which interrupts the deleterious inflammatory cycle and induces hyaluronic acid synthesis, thereby promoting cartilage repair [37]. Hypertonic glucose augmentation therapy (HDP) is also making clinical inroads with better efficacy and less risk of KOA compared to other injections and physical therapies [38]. Glucose also promotes ligament and tendon growth, fibroblast proliferation, extracellular matrix, and articular cartilage restoration by affecting growth factors [39].

Interestingly, one influence that cannot be ignored for the association between blood glucose and OA is the use of blood glucose control medications. Several studies have shown that metformin may influence the development and progression of OA through multiple ways. In the OA mice model, metformin upregulated phosphorylation and AMPK expression in articular cartilage, which attenuated abnormal subchondral bone remodeling induced by osteoclast activation and alleviated osteoarthritis [40, 41]. OA is also associated with cellular senescence, and metformin ameliorates senescence and attenuates progression of OA in adipose-derived mesenchymal stem cells [42]. Studies have shown that metformin also targets chondrocytes, synovial macrophages and adipocytes to reduce osteoarthritis [43, 44]. In addition, metformin inhibits chondrocyte pyroptosis and ferroptosis to alleviate subchondral osteosclerosis and abnormal angiogenesis, leading to the alleviation of OA [45, 46]. Clinical studies have also found that the use of metformin in patients with type 2 diabetes significantly reduces the risk of total joint replacement [47]. Metformin has demonstrated significant protective effects and therapeutic potential for OA, and individuals with abnormal blood glucose may have concomitant use of metformin and other medications, which may confound the relationship between blood glucose and OA.

The unclear relationship among dysglycemia, diabetes, and OA may be partly explained by the influence of confounding factors. Obesity, an epidemic with an annually increasing incidence, usually coexists with dysglycemia, is an important risk factor for OA [48]. On one hand, obesity increases the mechanical load on the joints and accelerates the progression of OA; on the other hand, obesity-induced systemic and localized inflammation can be damaging to the integrity of the extracartilaginous matrix [49, 50]. Several in vivo and in vitro studies have shown that impaired glucose metabolism, oxidative stress, and accumulation of advanced glycosylation products (AGEs) link OA to blood glucose [35, 51, 52]. However, even in animal experiments, it is difficult to rule out biomechanical effects of obesity on diabetes-induced metabolic alterations because all experimental animals in diabetes models are more obese than normal [53]. In addition, confounding factors such as socioeconomic status and occupation, which cannot be completely ruled out, may also affect the association between glycemic abnormalities and OA [54].

This study has several advantages. RCTs are costly and have ethical constraints that make it difficult to conduct many studies, whereas MR, as a method of epidemiological study with similarities, is promising for exploring the link between traits and diseases. Second, MR studies using genetic variants as IVs can better avoid confounding factors and reverse causality. Third, we selected large-sample GWAS datasets during the study’s implementation and rigorously screened for genetic variants using multiple procedures to improve the credibility of the study. Meanwhile, we applied multiple MR methods as well as sensitivity analysis methods to improve the robustness of the results.

This study has some limitations. Since MR methods rely on the cumulative effect of genetic variants over a lifetime, results may differ from clinically observed effects [55]. Since we selected the study sample from European ancestry, caution should be exercised in applying the results to populations of other ancestries.

Conclusion

This study used UVMR and MVMR methods to investigate the genetic causality between blood glucose-related traits and OA (HOA and KOA). We found significant negative genetic causal relationship of FG on HOA and KOA, and this association remained significant after adjusting for the effect of BMI.HbA1c also had an independent negative genetic causal effect on HOA after adjusting for BMI. At the same time, there was no evidence of reverse genetic causality for OA on blood glucose-related traits. This study provides new insights to further clarify the relationship between blood glucose levels and OA, as well as the pathogenesis, etiology and genetics of OA.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

2hGlu:

2 h-glucose post-challenge

AGEs:

Advanced glycosylation products

BMI:

Body mass index

CI:

Confidence interval

DM:

Diabetes mellitus

FG:

Fasting glucose

GWAS:

Genome-wide association study

HbA1c:

Glycated hemoglobin

HDP:

Hypertonic glucose augmentation therapy

HOA:

Hip osteoarthritis

IV:

Instrumental variable

IVW:

Inverse-variance weighted

KOA:

Knee osteoarthritis

MR:

Mendelian randomization

MVMR:

Multivariate mendelian randomization

OA:

Osteoarthritis

OR:

Odds ratio

RCTs:

Randomized controlled trials

SNP:

Single nucleotide polymorphism

UVMR:

Univariate mendelian randomization

References

  1. Hunter DJ, March L, Chew M. Osteoarthritis in 2020 and beyond: a Lancet Commission. Lancet. 2020;396:1711–2.

    Article  PubMed  Google Scholar 

  2. Katz JN, Arant KR, Loeser RF. Diagnosis and treatment of hip and knee osteoarthritis: a review. JAMA. 2021;325:568–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Mandl LA. Osteoarthritis year in review 2018: clinical. Osteoarthritis Cartilage. 2019;27:359–64.

    Article  CAS  PubMed  Google Scholar 

  4. Batushansky A, et al. Fundamentals of OA. An initiative of Osteoarthritis and Cartilage. Obesity and metabolic factors in OA. Osteoarthritis Cartilage. 2022;30:501–15.

    Article  CAS  PubMed  Google Scholar 

  5. Valdes AM. Metabolic syndrome and osteoarthritis pain: common molecular mechanisms and potential therapeutic implications. Osteoarthritis Cartilage. 2020;28:7–9.

    Article  CAS  PubMed  Google Scholar 

  6. Han H-S, Kang G, Kim JS, Choi BH, Koo S-H. Regulation of glucose metabolism from a liver-centric perspective. Exp Mol Med. 2016;48:e218.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Driban JB, et al. Glucose homeostasis influences the risk of incident knee osteoarthritis: data from the osteoarthritis initiative. J Orthop Res off Publ Orthop Res Soc. 2017;35:2282–7.

    Article  CAS  Google Scholar 

  8. Louati K, Vidal C, Berenbaum F, Sellam J. Association between diabetes mellitus and osteoarthritis: systematic literature review and meta-analysis. RMD Open. 2015;1:e000077.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Dawson LP, et al. Is abnormal glucose tolerance or diabetes a risk factor for knee, hip, or hand osteoarthritis? A systematic review. Semin Arthritis Rheum. 2018;48:176–89.

    Article  CAS  PubMed  Google Scholar 

  10. Li X, Zhou Y, Liu J. Association between prediabetes and osteoarthritis: a Meta-analysis. Horm Metab Res Horm Stoffwechselforschung Horm Metab. 2022;54:104–12.

    Article  Google Scholar 

  11. Sekula P, Del Greco M, Pattaro F, C., Köttgen A. Mendelian randomization as an Approach to assess causality using Observational Data. J Am Soc Nephrol JASN. 2016;27:3253–65.

    Article  PubMed  Google Scholar 

  12. Gupta V, Walia GK, Sachdeva MP. Mendelian randomization’: an approach for exploring causal relations in epidemiology. Public Health. 2017;145:113–9.

    Article  CAS  PubMed  Google Scholar 

  13. Larsson SC. Mendelian randomization as a tool for causal inference in human nutrition and metabolism. Curr Opin Lipidol. 2021;32:1–8.

    Article  CAS  PubMed  Google Scholar 

  14. Chen J, et al. The trans-ancestral genomic architecture of glycemic traits. Nat Genet. 2021;53:840–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Tachmazidou I, et al. Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank data. Nat Genet. 2019;51:230–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Locke AE, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Burgess S, Thompson SG, CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in mendelian randomization studies. Int J Epidemiol. 2011;40:755–64.

    Article  PubMed  Google Scholar 

  18. Qu Z, et al. A mendelian randomization study on the role of serum parathyroid hormone and 25-hydroxyvitamin D in osteoarthritis. Osteoarthritis Cartilage. 2021;29:1282–90.

    Article  CAS  PubMed  Google Scholar 

  19. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in mendelian randomization with some Invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40:304–14.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Burgess S, Thompson SG. Interpreting findings from mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32:377–89.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Xu J, et al. Genetic Causal Association between Iron Status and Osteoarthritis: a two-sample mendelian randomization. Nutrients. 2022;14:3683.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Verbanck M, Chen C-Y, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from mendelian randomization between complex traits and diseases. Nat Genet. 2018;50:693–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hemani G, et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife. 2018;7:e34408.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Zhang J. Mendelian randomization study implies causal linkage between telomere length and juvenile idiopathic arthritis in a European Population. J Inflamm Res. 2022;15:977–86.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Hemani G, Tilling K. Davey Smith, G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 2017;13:e1007081.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Bowden J, Holmes MV. Meta-analysis and mendelian randomization: a review. Res Synth Methods. 2019;10:486–96.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Halabitska I, Oksenych V, Kamyshnyi O. Exploring the efficacy of alpha-lipoic acid in Comorbid Osteoarthritis and Type 2 diabetes Mellitus. Nutrients. 2024;16:3349.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Li Z, Yin S, Zhao G, Cao X. Association between sarcopenic obesity and osteoarthritis: the potential mediating role of insulin resistance. Exp Gerontol. 2024;197:112611.

    Article  CAS  PubMed  Google Scholar 

  30. Zheng J, et al. Association of Diabetes Mellitus Status and Hyperglycemia with symptomatic knee osteoarthritis. Arthritis Care Res. 2023;75:509–18.

    Article  CAS  Google Scholar 

  31. Rogers-Soeder TS, et al. Association of Diabetes Mellitus and biomarkers of abnormal glucose metabolism with Incident Radiographic knee osteoarthritis. Arthritis Care Res. 2020;72:98–106.

    Article  CAS  Google Scholar 

  32. Konstari S, et al. Associations of metabolic syndrome and its components with the risk of incident knee osteoarthritis leading to hospitalization: a 32-Year follow-up study. Cartilage. 2021;13:S1445–56.

    Article  Google Scholar 

  33. Cui Z, et al. Type 2 diabetes and glycemic traits are not causal factors of Osteoarthritis: a two-sample mendelian randomization analysis. Front Genet. 2020;11:597876.

    Article  CAS  PubMed  Google Scholar 

  34. Tan C, Li L, Han J, Xu K, Liu X. A new strategy for osteoarthritis therapy: inhibition of glycolysis. Front Pharmacol. 2022;13:1057229.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Li K, et al. Impaired glucose metabolism underlies articular cartilage degeneration in osteoarthritis. FASEB J off Publ Fed Am Soc Exp Biol. 2022;36:e22377.

    CAS  Google Scholar 

  36. Rios-Arce ND, Hum NR, Loots GG. Interactions between diabetes Mellitus and Osteoarthritis: from animal studies to Clinical Data. JBMR Plus. 2022;6:e10626.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Rotter Sopasakis V, et al. Elevated glucose levels preserve glucose uptake, Hyaluronan Production, and low glutamate release following Interleukin-1β stimulation of differentiated chondrocytes. Cartilage. 2019;10:491–503.

    Article  CAS  PubMed  Google Scholar 

  38. Wang J, et al. Meta-analysis of clinical trials focusing on hypertonic dextrose prolotherapy (HDP) for knee osteoarthritis. Aging Clin Exp Res. 2022;34:715–24.

    Article  PubMed  Google Scholar 

  39. Pan P-J, Wang J-C, Tsai C-C, Kuo H-C. Identification of early response to hypertonic dextrose prolotherapy markers in knee osteoarthritis patients by an inflammation-related cytokine array. J Chin Med Assoc. 2022;85:525.

    Article  CAS  PubMed  Google Scholar 

  40. Guo H, et al. Metformin attenuates osteoclast-mediated abnormal subchondral bone remodeling and alleviates osteoarthritis via AMPK/NF-κB/ERK signaling pathway. PLoS ONE. 2021;16:e0261127.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Li J, et al. Metformin limits osteoarthritis development and progression through activation of AMPK signalling. Ann Rheum Dis. 2020;79:635–45.

    Article  CAS  PubMed  Google Scholar 

  42. Li Z et al. Metformin Ameliorates Senescence of Adipose-Derived Mesenchymal Stem Cells and Attenuates Osteoarthritis Progression via the AMPK-Dependent Autophagy Pathway. Oxid. Med. Cell. Longev. 2022, 4620254 (2022).

  43. Zheng M, et al. Metformin attenuates the inflammatory response via the regulation of synovial M1 macrophage in Osteoarthritis. Int J Mol Sci. 2023;24:5355.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Li D, et al. Metformin attenuates osteoarthritis by targeting chondrocytes, synovial macrophages and adipocytes. Rheumatol Oxf Engl. 2023;62:1652–61.

    Article  Google Scholar 

  45. Yan J, Feng G, Ma L, Chen Z, Jin Q. Metformin alleviates osteoarthritis in mice by inhibiting chondrocyte ferroptosis and improving subchondral osteosclerosis and angiogenesis. J Orthop Surg. 2022;17:333.

    Article  CAS  Google Scholar 

  46. Yan J, et al. Metformin reduces chondrocyte pyroptosis in an osteoarthritis mouse model by inhibiting NLRP3 inflammasome activation. Exp Ther Med. 2022;23:222.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Zhu Z, et al. Metformin use and associated risk of total joint replacement in patients with type 2 diabetes: a population-based matched cohort study. CMAJ Can Med Assoc J J Assoc Medicale Can. 2022;194:E1672–84.

    Article  CAS  Google Scholar 

  48. Kolb H, Martin S. Environmental/lifestyle factors in the pathogenesis and prevention of type 2 diabetes. BMC Med. 2017;15:131.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Martel-Pelletier J, et al. Nat Rev Dis Primer. 2016;2:16072. Osteoarthritis.

    Article  Google Scholar 

  50. Reyes C, et al. Association between Overweight and Obesity and risk of clinically diagnosed knee, hip, and Hand Osteoarthritis: a Population-based Cohort Study. Arthritis Rheumatol Hoboken NJ. 2016;68:1869–75.

    Article  Google Scholar 

  51. Li Q, et al. Hyperglycemia-induced accumulation of advanced glycosylation end products in fibroblast-like synoviocytes promotes knee osteoarthritis. Exp Mol Med. 2021;53:1735–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Laiguillon M-C, et al. Characterization of diabetic osteoarthritic cartilage and role of high glucose environment on chondrocyte activation: toward pathophysiological delineation of diabetes mellitus-related osteoarthritis. Osteoarthritis Cartilage. 2015;23:1513–22.

    Article  PubMed  Google Scholar 

  53. Khor A, Ma C-A, Hong C, Hui LL-Y, Leung Y. Y. Diabetes mellitus is not a risk factor for osteoarthritis. RMD Open. 2020;6:e001030.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Frey N, Hügle T, Jick SS, Meier CR, Spoendlin J. Type II diabetes mellitus and incident osteoarthritis of the hand: a population-based case-control analysis. Osteoarthritis Cartilage. 2016;24:1535–40.

    Article  CAS  PubMed  Google Scholar 

  55. Gill D, Walker VM, Martin RM, Davies NM, Tzoulaki I. Comparison with randomized controlled trials as a strategy for evaluating instruments in mendelian randomization. Int J Epidemiol. 2020;49:1404–6.

    Article  PubMed  Google Scholar 

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Acknowledgements

We are grateful for the published data and cited studies used in the article.

Funding

This work was financially supported by the National Natural Science Foundation of China (No. 82072432).

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Authors and Affiliations

  1. Department of Joint Surgery, Honghui Hospital, Xi’an Jiaotong University, Xi’an, Shaanxi, 710054, China

    Junxiang Wang, Mingyi Yang, Jiachen Wang, Ruoyang Feng, Ke Xu & Peng Xu

  2. Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China

    Junxiang Wang & Leixuan Peng

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  1. Junxiang Wang
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  2. Leixuan Peng
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  3. Mingyi Yang
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  4. Jiachen Wang
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  6. Ke Xu
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  7. Peng Xu
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Contributions

Study design: JXW MY PX.Data acquisition: JXW RF LP.Drafting the manuscript: JXW LP.Reviewing & editing: JCW KX PX.Final approval: JXW LP MY JCW RF KX PXAll authors reviewed the manuscript.

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Correspondence to Mingyi Yang or Peng Xu.

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Wang, J., Peng, L., Yang, M. et al. Is there a genetic relationship between blood glucose and osteoarthritis? A mendelian randomization study. Diabetol Metab Syndr 16, 274 (2024). https://doi.org/10.1186/s13098-024-01517-3

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Keywords

Diabetology & Metabolic Syndrome

ISSN: 1758-5996

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